Triple
T10512427
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Flying High |
E247947
|
entity |
| Predicate | hasFilmSongType |
P68360
|
FINISHED |
| Object | Hollywood song-and-dance number |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Hollywood song-and-dance number | Statement: [Flying High, hasFilmSongType, Hollywood song-and-dance number]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFilmSongType Context triple: [Flying High, hasFilmSongType, Hollywood song-and-dance number]
-
A.
hasMusicFilm
Indicates a relationship where a subject is associated with or linked to a film that features or centers around music.
-
B.
hasSingingVoiceType
Indicates the specific category or classification of a person's singing voice (such as soprano, tenor, etc.) in relation to that person.
-
C.
songFeaturedInFilm
Indicates that a particular song is included or used within a specific film.
-
D.
hasMusicFilmSeries
Indicates that a subject is associated with or includes a series of films that are centered around music or musical themes.
-
E.
isFromMusicalFilm
chosen
Indicates that something originates from, or is part of, a musical film production.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69d381c4aa948190942e1d803143fb0e |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d509b5fcb8819087a23a2b26aecd70 |
completed | April 7, 2026, 1:42 p.m. |
| PD | Predicate disambiguation | batch_69d4fb919ea08190bcc1193e2014d437 |
completed | April 7, 2026, 12:41 p.m. |
Created at: April 6, 2026, 12:27 p.m.